EMPIAR-13424 - Cryo-EM structure of the aspartate:alanine antiporter AspT mutant L60 [8800 multi-frame micrographs composed of 60 frames each in TIFF format]

EMPIAR-13424

Cryo-EM structure of the aspartate:alanine antiporter AspT mutant L60 [8800 multi-frame micrographs composed of 60 frames each in TIFF format]

Publication:

Elucidation of the structure and molecular mechanisms of the aspartate antiporter

Nanatani K, Guan L, Kanno R, Kawabata T, Watanabe S, Katsube S, Hariharan P, Hidaka M, Yamanaka T, Toda K, Fujiki T, Kunii K, Miyamoto A, Chiba F, Ogasawara S, Murata T, Inaba K, Mitsuoka K, Abe K, Yamamoto M, Koshiba S

Communications biology 8 (2025)

PMID: 40998974

DOI: 10.1038/s42003-025-08676-7

Related EMDB entry:

EMD-64996

Deposited:

2025-10-01

Released:

2026-04-08

Last modified:

2026-04-08

Imageset size:

2.18 TB

Imageset DOI:

10.6019/EMPIAR-13424

Experimental metadata:

Download xml json

Deposited via PDBj

Contains:

micrographs - multiframe

Image Sets

1. Cryo-EM structure of the aspartate:alanine antiporter AspT mutant L60C

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

8800

Image size:

(5760, 4092)

Pixel type:

UNSIGNED 16 BIT INTEGER

Pixel spacing:

(0.788, 0.788)

Details:

- Microscope: JEOL CRYO ARM 300
- Pixel size: 0.788 Ang/pixel
- Voltage: 300 kV
- Spherical aberration: 2.7
- Dose rate: 1.0 e / A**2 / frame
- Gain_file: CountCDSRef_20210521L60CV2sec_289-1_000.mrc without rotation and Y-flipping
- Please ignore the pixel size in the header; use 0.788 A/px.

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Citations

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

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Deng Y, Wang S, Xiang M, Li Y, Zhuo L, Cao D, Fu X, Zou Q. (2026)

Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank.

Fonseca N, Duraisamy AK, Wang Z, Somasundharam S, Tayebinia M, de Oliveira LC, Ma M, Turner J, Patwardhan A, Kleywegt GJ, Hartley M, Morris KL. (2026)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.

Jones HN, Deshmukh A, Pande K. (2026)

CryoGS: High-Quality Cryo-EM Homogeneous Reconstruction by Gaussian Splatting

Zeng B, Liu S, Cheng S, Xu G, Fan H. (2026)

Point cloud deformation modeling for particle selection following cryo-EM 2D classification.

Wang X, Mo Z, Li F, Zhang F, Wan X. (2026)

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2026)

CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection.

Schäfer JH, Calza A, Hom K, Damodar P, Peng R, Bogdanović N, Lander GC, Stagg SM, Cianfrocco MA. (2025)